Ishak Abdul Azid

Optimization of thermal resistance of stacked micro‐channel using genetic algorithms

Purpose – To determine the optimal dimensions for a stacked micro‐channel using the genetic algorithms (GAs) under different flow constraints.Design/methodology/approach – GA is used as an optimization tool for optimizing the thermal resistance of a stacked micro‐channel under different flow constraints obtained by using the one dimensional (1D) and two dimensional (2D) finite element methods (FEM) and by thermal resistance network model as well (proposed by earlier researcher). The 2D FEM is used to study the effect of two dimensional heat conduction in the micro‐channel material. Some parametric studies are carried out to determine the resulting performance of the stacked micro‐channel. Different number of layers of the stacked micro‐channel is also investigated to study its effect on the minimum thermal resistance.Findings – The results obtained from the 1D FEM analysis compare well with those obtained from the thermal resistance network model. However, the 2D FEM analysis results in lower thermal resi...

Determination of thermal compact model via evolutionary genetic optimization method

Genetic Algorithms (GA) are adaptive search algorithms based on the theory of natural selection and survival of the fittest. In this study, GA was used to derive a thermal compact model of a micro lead frame package. The GA derived model was then used to compute the junction temperature (T/sub j/) of the package for various boundary conditions. The results obtained were checked against simulation results of a detailed thermal model and were found to be within /spl plusmn/1.5% of error. Computational time taken by the detailed finite element model required approximately 4 min whereas the GA derived model took less than 35 s to generate the T/sub j/ of the package. Further, the study shows the feasibility and potential of applying GA as a powerful tool for optimization.

ISO 13485:2003: Implementation reference model from the Malaysian SMEs medical device industry

Purpose – The purpose of this paper is to provide an understanding on implementation and operation of ISO 13485:2003 – “Medical Devices – Quality Management System – Requirements for Regulatory Purposes” – in the perspective of medical device industries in Malaysia. The study is focused on the Malaysian Small and Medium Enterprises (SMEs) which currently have accredited to ISO 9001:2000 quality management systems.Design/methodology/approach – Literature research and comparative analysis between ISO 13485:2003 and ISO 9001:2000 standard and requirements. A reference model is developed to assist the Malaysian SMEs towards ISO 13485:2003 accreditation.Findings – Unlike ISO 9001:2000, ISO 13485:2003 stresses the safety and efficacy of medical devices that are being produced. For this reason risk management is an essential process that needs to be adopted into the ISO 13485:2003 quality management system. Moreover, to demonstrate the effectiveness of the ISO 13485:2003 implementation, this standard has placed ...

Piezoresistive effects in controllable defective HFTCVD graphene-based flexible pressure sensor

In this work, the piezoresistive effects of defective graphene used on a flexible pressure sensor are demonstrated. The graphene used was deposited at substrate temperatures of 750, 850 and 1000 °C using the hot-filament thermal chemical vapor deposition method in which the resultant graphene had different defect densities. Incorporation of the graphene as the sensing materials in sensor device showed that a linear variation in the resistance change with the applied gas pressure was obtained in the range of 0 to 50 kPa. The deposition temperature of the graphene deposited on copper foil using this technique was shown to be capable of tuning the sensitivity of the flexible graphene-based pressure sensor. We found that the sensor performance is strongly dominated by the defect density in the graphene, where graphene with the highest defect density deposited at 750 °C exhibited an almost four-fold sensitivity as compared to that deposited at 1000 °C. This effect is believed to have been contributed by the scattering of charge carriers in the graphene networks through various forms such as from the defects in the graphene lattice itself, tunneling between graphene islands, and tunneling between defect-like structures.

Effects of hybrid fillers based on micro- and nano-sized silver particles on the electrical performance of epoxy composites

In the present study, electrically conductive adhesives produced from hybrid fillers based on micro- and nano-sized silver (Ag) was developed. The influence of the hybrid filler composition on the electrical properties of the hybrid system was studied. The electrical conductivity of the epoxy composites filled with micro- and nano-silver was correlated with their morphologies. A positive effect was observed in the electrical conductivity result when the composition of micro- and nano-sized Ag particles reached a 50:50 weight ratio. The nano-sized Ag particles became interconnecting particles in the interstitial spaces between micro-sized particles. Micrograph scanning shows that the particles were well distributed and dispersed, the separation between lumps of Ag filler by the insulating matrix was significantly reduced, leading to the formation of continuous linkages. The increased electrical conductivity resulted in a charge around the particle distribution, which led to the high capacity. Hence, these particles increased the conductivity of the system.

MAINTENANCE POLICY SELECTION MODEL-A CASE STUDY IN THE PALM OIL INDUSTRY

– An optimal maintenance policy is key to the improvement of the availability and reliability of a system at an acceptable level without a significant increase in investment. However, the selection process is a complicated task because it requires in-depth knowledge on maintenance policies and on the technical requirements of maintenance. The difficulties and complexity of the selection process arise from the combination of conflicting maintenance constraints such as available spares, size of workforce, and maintenance skills. The paper aims to discuss these issues. , – The proposed maintenance policy selection (MPS) model is separated into three major phases. The first phase identifies the critical system (CS) based on failure frequency. The failure mechanism in the CS is then analyzed by using a failure mode and effect analysis in the second phase. In the third phase, a multi-criteria decision making method, called the technique for order of preference by similarity to ideal solution, is adopted to identify an optimal maintenance policy that can minimize the failures. , – Through a case study, preventive maintenance was selected as the optimal maintenance policy for the reduction of system failures. The results obtained from the case study not only provide evidence of the feasibility and practicability of the developed model, but also test the acceptability and rationale of the developed model from the industry perspective. Valuable knowledge and experience from employees were extracted and utilized through the proposed model to rank the optimal maintenance policy based on the capability to reduce failure. , – The practicality of the MPS model is justified through an implementation in the palm oil industry. The application of the MPS model can also be extended to other manufacturing industries.

Neuro‐genetic optimization of micro compact heat exchanger

Purpose – This paper seeks to introduce an optimization method for maximizing the effectiveness of the micro compact heat exchanger (MHE) under various geometrical parameters.Design/methodology/approach – Optimization is realized using the neuro‐genetic methodology which combines the application of artificial neural network (ANN) together with genetic algorithms (GA). The analyses are divided into two main sections; the first being the modeling and prediction using finite element method, the second being the neuro‐genetic optimization. Initial results obtained from the finite element modeling are utilized for training in ANN. Subsequently, optimization is done using GA, once a well trained ANN is achieved.Findings – ANN accurately predicts the effectiveness of the MHE and compares well with those obtained from the finite element simulation. Optimization shows a significant improvement in the maximum effectiveness of the MHE achievable for the given range of input parameters. Additionally, computational ef...

Maintenance policy selection: a review towards building proper selection model

In the past few decades, various maintenance policies with different operation principles, such as preventive or predictive maintenance were developed due to importance of maintenance in current industries. However, the implementation of a maintenance policy must suit the specific conditions and constraints of a specific company. Thus it is important to identify and then select the optimal maintenance policy for a system in order to gain maximum effectiveness. This paper reviews current research on selecting the optimal maintenance policy in various industries and recommendations for future research regarding new methods of selecting an optimal maintenance.

Development of autonomous maintenance implementation framework for semiconductor industries

Total productive maintenance (TPM) is one of the maintenance strategies that aim to increase availability and reliability of production machines/equipment. The key to TPM success is the development of autonomous maintenance (AM) practice. The AM refers to human capital development among operators supported by technicians and engineers to perform easy daily maintenance activities aside from planned maintenance. This paper presents the implementation process of AM in a semiconductor company in Malaysia. An AM implementation framework is developed based on four systematic stages: AM initial preparation, AM training and motivation, AM five-step execution and AM audit. The framework is implemented in one of the production lines identified as the most critical area in the company chosen as the case study. The findings based on the developed AM framework are reported in this paper.

Preventive replacement schedule: a case study at a processing industry

The application of the preventive maintenance (PM) strategy to reduce machine breakdown problems has been discussed in many studies. One of the popular strategies that widely use it is preventive replacement (PR), which aims to determine the optimum replacement time. The critical issue, however, is that most studies assume the aging (lifetime) of a component to be time dependent. In reality, the failure of a component is influenced by an external factor (covariate). This paper presents the process of revising or updating the PR time by considering external factors (the covariates effect) by using the proportional hazard model (PHM). The revised PR time is determined based on the age replacement model (ARM) and block replacement model (BRM). The effects of the revised PR time on the reliability cycle and cost reduction for both ARM and BRM are compared. The results show that the application of ARM is more beneficial in terms of cost saving, while the application of BRM results in a higher reliability cycle. A corresponding case study in the processing industry is presented.